Timosaponin AⅢ induces drug-metabolizing enzymes by activating constitutive androstane receptor (CAR) via dephosphorylation of the EGFR signaling pathway

The current study aimed to assess the effect of timosaponin AⅢ(T-AⅢ)on drug-metabolizing enzymes during anticancer therapy.The in vivo experiments were conducted on nude and ICR mice.Following a 24-day administration of T-AⅢ,the nude mice exhibited an induction of CYP2B10,MDR1,and CYP3A11 expression in the liver tissues.In the ICR mice,the expression levels of CYP2B10 and MDR1 increased after a three-day T-AⅢ administration.The in vitro assessments with HepG2 cells revealed that T-AⅢ induced the expression of CYP2B6,MDR1,and CYP3A4,along with constitutive androstane receptor(CAR)activation.Treatment with CAR siRNA reversed the T-AⅢ-induced increases in CYP2B6 and CYP3A4 expression.Furthermore,other CAR target genes also showed a significant increase in the expression.The up-regulation of murine CAR was observed in the liver tissues of both nude and ICR mice.Subsequent findings demonstrated that T-AⅢ activated CAR by inhibiting ERK1/2 phosphorylation,with this effect being partially reversed by the ERK activator t-BHQ.Inhibition of the ERK1/2 signaling pathway was also observed in vivo.Additionally,T-AⅢ inhibited the phosphorylation of EGFR at Tyr1173 and Tyr845,and suppressed EGF-induced phosphorylation of EGFR,ERK,and CAR.In the nude mice,T-AⅢ also inhibited EGFR phosphorylation.These results collectively indicate that T-AⅢ is a novel CAR activator through inhibition of the EGFR pathway.

Digital signal acquisition system for complex nuclear reaction experiments

A digital data-acquisition system based on XIA LLC products was used in a complex nuclear reaction experiment using radioactive ion beams.A flexible trigger system based on a field-programmable gate array(FPGA)parametrization was developed to adapt to different experimental sizes.A user-friendly interface was implemented,which allows converting script language expressions into FPGA internal control parameters.The proposed digital system can be combined with a conventional analog data acquisition system to provide more flexibility.The performance of the combined system was veri-fied using experimental data.

PHD17 acts as a target of miR1320 to negatively control cold tolerance via JA-activated signaling in rice

Plant Homeo Domain(PHD)proteins are involved in diverse biological processes during plant growth.However,the regulation of PHD genes on rice cold stress response remains largely unknown.Here,we reported that PHD17 negatively regulated cold tolerance in rice seedlings as a cleavage target of miR1320.PHD17 expression was greatly induced by cold stress,and was down-regulated by miR1320 overexpression and up-regulated by miR1320 knockdown.Through 5'RACE and dual luciferase assays,we found that miR1320 targeted and cleaved the 3'UTR region of PHD17.PHD17 was a nuclearlocalized protein and acted as a transcriptional activator in yeast.PHD17 overexpression reduced cold tolerance of rice seedlings,while knockout of PHD17 increased cold tolerance,partially via the CBF cold signaling.By combining transcriptomic and physiological analyses,we demonstrated that PHD17 modulated ROS homeostasis and flavonoid accumulation under cold stress.K-means clustering analysis revealed that differentially expressed genes in PHD17 transgenic lines were significantly enriched in the jasmonic acid(JA)biosynthesis pathway,and expression of JA biosynthesis and signaling genes was verified to be affected by PHD17.Cold stress tests applied with MeJA or IBU(JA synthesis inhibitor)further suggested the involvement of PHD17 in JA-mediated cold signaling.Taken together,our results suggest that PHD17 acts downstream of miR1320 and negatively regulates cold tolerance of rice seedlings through JA-mediated signaling pathway.

Calmodulins and calmodulin-like proteins-mediated plant organellar calcium signaling networks under abiotic stress

Plant calmodulins(CaMs)and calmodulin-like proteins(CMLs)mediate Ca~(2+)signaling in response to abiotic stresses.Manipulation of this signaling in crops could increase stress tolerance.We review methods for detecting Ca~(2+)signals,regulatory roles of Ca Ms and CMLs,binding targets,and Ca~(2+)networks under abiotic stress in organelles.

Gut microbiota dysbiosis contributes toα-synuclein-related pathology associated with C/EBPβ/AEP signaling activation in a mouse model of Parkinson’s disease

Parkinson’s disease is a neurodegenerative disease characterized by motor and gastrointestinal dysfunction.Gastrointestinal dysfunction can precede the onset of motor symptoms by several years.Gut microbiota dysbiosis is involved in the pathogenesis of Parkinson’s disease,whether it plays a causal role in motor dysfunction,and the mechanism underlying this potential effect,remain unknown.CCAAT/enhancer binding proteinβ/asparagine endopeptidase(C/EBPβ/AEP)signaling,activated by bacterial endotoxin,can promoteα-synuclein transcription,thereby contributing to Parkinson’s disease pathology.In this study,we aimed to investigate the role of the gut microbiota in C/EBPβ/AEP signaling,α-synuclein-related pathology,and motor symptoms using a rotenone-induced mouse model of Parkinson’s disease combined with antibiotic-induced microbiome depletion and fecal microbiota transplantation.We found that rotenone administration resulted in gut microbiota dysbiosis and perturbation of the intestinal barrier,as well as activation of the C/EBP/AEP pathway,α-synuclein aggregation,and tyrosine hydroxylase-positive neuron loss in the substantia nigra in mice with motor deficits.However,treatment with rotenone did not have any of these adverse effects in mice whose gut microbiota was depleted by pretreatment with antibiotics.Importantly,we found that transplanting gut microbiota derived from mice treated with rotenone induced motor deficits,intestinal inflammation,and endotoxemia.Transplantation of fecal microbiota from healthy control mice alleviated rotenone-induced motor deficits,intestinal inflammation,endotoxemia,and intestinal barrier impairment.These results highlight the vital role that gut microbiota dysbiosis plays in inducing motor deficits,C/EBPβ/AEP signaling activation,andα-synuclein-related pathology in a rotenone-induced mouse model of Parkinson’s disease.Additionally,our findings suggest that supplementing with healthy microbiota may be a safe and effective treatment that could help ameliorate the progression of motor deficits in patients with Parkinson’s disease.

Hypoglycemic mechanism of Tegillarca granosa polysaccharides on type 2 diabetic mice by altering gut microbiota and regulating the PI3K-akt signaling pathwaye

Type 2 diabetes mellitus(T2DM)is a complex metabolic disease threatening human health.We investigated the effects of Tegillarca granosa polysaccharide(TGP)and determined its potential mechanisms in a mouse model of T2DM established through a high-fat diet and streptozotocin.TGP(5.1×10^(3) Da)was composed of mannose,glucosamine,rhamnose,glucuronic acid,galactosamine,glucose,galactose,xylose,and fucose.It could significantly alleviate weight loss,reduce fasting blood glucose levels,reverse dyslipidemia,reduce liver damage from oxidative stress,and improve insulin sensitivity.RT-PCR and Western blotting indicated that TGP could activate the phosphatidylinositol-3-kinase/protein kinase B signaling pathway to regulate disorders in glucolipid metabolism and improve insulin resistance.TGP increased the abundance of Allobaculum,Akkermansia,and Bifidobacterium,restored the microbiota abundance in the intestinal tracts of mice with T2DM,and promoted short-chain fatty acid production.This study provides new insights into the antidiabetic effects of TGP and highlights its potential as a natural hypoglycemic nutraceutical.

Argatroban promotes recovery of spinal cord injury by inhibiting the PAR1/JAK2/STAT3 signaling pathway

Argatroban is a synthetic thrombin inhibitor approved by U.S.Food and Drug Administration for the treatment of thrombosis.However,whether it plays a role in the repair of spinal cord injury is unknown.In this study,we established a rat model of T10 moderate spinal cord injury using an NYU Impactor ModerⅢand performed intraperitoneal injection of argatroban for 3 consecutive days.Our results showed that argatroban effectively promoted neurological function recovery after spinal cord injury and decreased thrombin expression and activity in the local injured spinal cord.RNA sequencing transcriptomic analysis revealed that the differentially expressed genes in the argatroban-treated group were enriched in the JAK2/STAT3 pathway,which is involved in astrogliosis and glial scar formation.Western blotting and immunofluorescence results showed that argatroban downregulated the expression of the thrombin receptor PAR1 in the injured spinal cord and the JAK2/STAT3 signal pathway.Argatroban also inhibited the activation and proliferation of astrocytes and reduced glial scar formation in the spinal cord.Taken together,these findings suggest that argatroban may inhibit astrogliosis by inhibiting the thrombin-mediated PAR1/JAK2/STAT3 signal pathway,thereby promoting the recovery of neurological function after spinal cord injury.

Melatonin improves synapse development by PI3K/Akt signaling in a mouse model of autism spectrum disorder

Autism spectrum disorders are a group of neurodevelopmental disorders involving more than 1100 genes,including Ctnnd2 as a candidate gene.Ctnnd2knockout mice,serving as an animal model of autis m,have been demonstrated to exhibit decreased density of dendritic spines.The role of melatonin,as a neuro hormone capable of effectively alleviating social interaction deficits and regulating the development of dendritic spines,in Ctnnd2 deletion-induced nerve injury remains unclea r.In the present study,we discove red that the deletion of exon 2 of the Ctnnd2 gene was linked to social interaction deficits,spine loss,impaired inhibitory neurons,and suppressed phosphatidylinositol-3-kinase(PI3K)/protein kinase B(Akt) signal pathway in the prefrontal cortex.Our findings demonstrated that the long-term oral administration of melatonin for 28 days effectively alleviated the aforementioned abnormalities in Ctnnd2 gene-knockout mice.Furthermore,the administration of melatonin in the prefro ntal cortex was found to improve synaptic function and activate the PI3K/Akt signal pathway in this region.The pharmacological blockade of the PI3K/Akt signal pathway with a PI3K/Akt inhibitor,wo rtmannin,and melatonin receptor antagonists,luzindole and 4-phenyl-2-propionamidotetralin,prevented the melatonin-induced enhancement of GABAergic synaptic function.These findings suggest that melatonin treatment can ameliorate GABAe rgic synaptic function by activating the PI3K/Akt signal pathway,which may contribute to the improvement of dendritic spine abnormalities in autism spectrum disorders.

Fanlian Huazhuo Formula alleviates high-fat diet-induced nonalcoholic fatty liver disease by modulating autophagy and lipid synthesis signaling pathway

BACKGROUND Fanlian Huazhuo Formula(FLHZF)has the functions of invigorating spleen and resolving phlegm,clearing heat and purging turbidity.It has been identified to have therapeutic effects on type 2 diabetes mellitus(T2DM)in clinical application.Non-alcoholic fatty liver disease(NAFLD)is frequently diagnosed in patients with T2DM.However,the therapeutic potential of FLHZF on NAFLD and the underlying mechanisms need further investigation.AIM To elucidate the effects of FLHZF on NAFLD and explore the underlying hepatoprotective mechanisms in vivo and in vitro.METHODS HepG2 cells were treated with free fatty acid for 24 hours to induce lipid accumulation cell model.Subsequently,experiments were conducted with the different concentrations of freeze-dried powder of FLHZF for 24 hours.C57BL/6 mice were fed a high-fat diet for 8-week to establish a mouse model of NAFLD,and then treated with the different concentrations of FLHZF for 10 weeks.RESULTS FLHZF had therapeutic potential against lipid accumulation and abnormal changes in biochemical indicators in vivo and in vitro.Further experiments verified that FLHZF alleviated abnormal lipid metabolism might by reducing oxidative stress,regulating the AMPKα/SREBP-1C signaling pathway,activating autophagy,and inhibiting hepatocyte apoptosis.CONCLUSION FLHZF alleviates abnormal lipid metabolism in NAFLD models by regulating reactive oxygen species,autophagy,apoptosis,and lipid synthesis signaling pathways,indicating its potential for clinical application in NAFLD.

Na^(+)/K^(+)-ATPase:ion pump,signal transducer,or cytoprotective protein,and novel biological functions

Na^(+)/K^(+)-ATPase is a transmembrane protein that has important roles in the maintenance of electrochemical gradients across cell membranes by transporting three Na^(+)out of and two K^(+)into cells.Additionally,Na^(+)/K^(+)-ATPase participates in Ca^(2+)-signaling transduction and neurotransmitter release by coordinating the ion concentration gradient across the cell membrane.Na^(+)/K^(+)-ATPase works synergistically with multiple ion channels in the cell membrane to form a dynamic network of ion homeostatic regulation and affects cellular communication by regulating chemical signals and the ion balance among different types of cells.Therefo re,it is not surprising that Na^(+)/K^(+)-ATPase dysfunction has emerged as a risk factor for a variety of neurological diseases.However,published studies have so far only elucidated the important roles of Na^(+)/K^(+)-ATPase dysfunction in disease development,and we are lacking detailed mechanisms to clarify how Na^(+)/K^(+)-ATPase affects cell function.Our recent studies revealed that membrane loss of Na^(+)/K^(+)-ATPase is a key mechanism in many neurological disorders,particularly stroke and Parkinson's disease.Stabilization of plasma membrane Na^(+)/K^(+)-ATPase with an antibody is a novel strategy to treat these diseases.For this reason,Na^(+)/K^(+)-ATPase acts not only as a simple ion pump but also as a sensor/regulator or cytoprotective protein,participating in signal transduction such as neuronal autophagy and apoptosis,and glial cell migration.Thus,the present review attempts to summarize the novel biological functions of Na^(+)/K^(+)-ATPase and Na^(+)/K^(+)-ATPase-related pathogenesis.The potential for novel strategies to treat Na^(+)/K^(+)-ATPase-related brain diseases will also be discussed.

Spi1 regulates the microglial/macrophage inflammatory response via the PI3K/AKT/mTOR signaling pathway after intracerebral hemorrhage

Preclinical and clinical studies have shown that microglia and macrophages participate in a multiphasic brain damage repair process following intracerebral hemorrhage.The E26 transformation-specific sequence-related transcription factor Spi1 regulates microglial/macrophage commitment and maturation.However,the effect of Spi1 on intracerebral hemorrhage remains unclear.In this study,we found that Spi1 may regulate recovery from the neuroinflammation and neurofunctional damage caused by intracerebral hemorrhage by modulating the microglial/macrophage transcriptome.We showed that high Spi1expression in microglia/macrophages after intracerebral hemorrhage is associated with the activation of many pathways that promote phagocytosis,glycolysis,and autophagy,as well as debris clearance and sustained remyelination.Notably,microglia with higher levels of Soil expression were chara cterized by activation of pathways associated with a variety of hemorrhage-related cellular processes,such as complement activation,angiogenesis,and coagulation.In conclusion,our results suggest that Spi1 plays a vital role in the microglial/macrophage inflammatory response following intracerebral hemorrhage.This new insight into the regulation of Spi1 and its target genes may advance our understanding of neuroinflammation in intracerebral hemorrhage and provide therapeutic targets for patients with intracerebral hemorrhage.

Detection method of forward-scatter signal based on Rényi entropy

The application scope of the forward scatter radar(FSR)based on the Global Navigation Satellite System(GNSS)can be expanded by improving the detection capability.Firstly,the forward-scatter signal model when the target crosses the baseline is constructed.Then,the detection method of the for-ward-scatter signal based on the Rényi entropy of time-fre-quency distribution is proposed and the detection performance with different time-frequency distributions is compared.Simula-tion results show that the method based on the smooth pseudo Wigner-Ville distribution(SPWVD)can achieve the best perfor-mance.Next,combined with the geometry of FSR,the influence on detection performance of the relative distance between the target and the baseline is analyzed.Finally,the proposed method is validated by the anechoic chamber measurements and the results show that the detection ability has a 10 dB improvement compared with the common constant false alarm rate(CFAR)detection.

Part-Whole Relational Few-Shot 3D Point Cloud Semantic Segmentation

This paper focuses on the task of few-shot 3D point cloud semantic segmentation.Despite some progress,this task still encounters many issues due to the insufficient samples given,e.g.,incomplete object segmentation and inaccurate semantic discrimination.To tackle these issues,we first leverage part-whole relationships into the task of 3D point cloud semantic segmentation to capture semantic integrity,which is empowered by the dynamic capsule routing with the module of 3D Capsule Networks(CapsNets)in the embedding network.Concretely,the dynamic routing amalgamates geometric information of the 3D point cloud data to construct higher-level feature representations,which capture the relationships between object parts and their wholes.Secondly,we designed a multi-prototype enhancement module to enhance the prototype discriminability.Specifically,the single-prototype enhancement mechanism is expanded to the multi-prototype enhancement version for capturing rich semantics.Besides,the shot-correlation within the category is calculated via the interaction of different samples to enhance the intra-category similarity.Ablation studies prove that the involved part-whole relations and proposed multi-prototype enhancement module help to achieve complete object segmentation and improve semantic discrimination.Moreover,under the integration of these two modules,quantitative and qualitative experiments on two public benchmarks,including S3DIS and ScanNet,indicate the superior performance of the proposed framework on the task of 3D point cloud semantic segmentation,compared to some state-of-the-art methods.

Comparing Fine-Tuning, Zero and Few-Shot Strategies with Large Language Models in Hate Speech Detection in English

Large Language Models(LLMs)are increasingly demonstrating their ability to understand natural language and solve complex tasks,especially through text generation.One of the relevant capabilities is contextual learning,which involves the ability to receive instructions in natural language or task demonstrations to generate expected outputs for test instances without the need for additional training or gradient updates.In recent years,the popularity of social networking has provided a medium through which some users can engage in offensive and harmful online behavior.In this study,we investigate the ability of different LLMs,ranging from zero-shot and few-shot learning to fine-tuning.Our experiments show that LLMs can identify sexist and hateful online texts using zero-shot and few-shot approaches through information retrieval.Furthermore,it is found that the encoder-decoder model called Zephyr achieves the best results with the fine-tuning approach,scoring 86.811%on the Explainable Detection of Online Sexism(EDOS)test-set and 57.453%on the Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter(HatEval)test-set.Finally,it is confirmed that the evaluated models perform well in hate text detection,as they beat the best result in the HatEval task leaderboard.The error analysis shows that contextual learning had difficulty distinguishing between types of hate speech and figurative language.However,the fine-tuned approach tends to produce many false positives.

A Graph-Based Semi-Supervised Approach for Few-Shot Class-Incremental Modulation Classification

With the successive application of deep learning(DL)in classification tasks,the DL-based modulation classification method has become the preference for its state-of-the-art performance.Nevertheless,once the DL recognition model is pre-trained with fixed classes,the pre-trained model tends to predict incorrect results when identifying incremental classes.Moreover,the incremental classes are usually emergent without label information or only a few labeled samples of incremental classes can be obtained.In this context,we propose a graphbased semi-supervised approach to address the fewshot classes-incremental(FSCI)modulation classification problem.Our proposed method is a twostage learning method,specifically,a warm-up model is trained for classifying old classes and incremental classes,where the unlabeled samples of incremental classes are uniformly labeled with the same label to alleviate the damage of the class imbalance problem.Then the warm-up model is regarded as a feature extractor for constructing a similar graph to connect labeled samples and unlabeled samples,and the label propagation algorithm is adopted to propagate the label information from labeled nodes to unlabeled nodes in the graph to achieve the purpose of incremental classes recognition.Simulation results prove that the proposed method is superior to other finetuning methods and retrain methods.

2D DOA Estimation of Coherent Signals with a Separated Linear Acoustic Vector-Sensor Array

In this paper, a two-dimensional(2D) DOA estimation algorithm of coherent signals with a separated linear acoustic vector-sensor(AVS) array consisting of two sparse AVS arrays is proposed. Firstly,the partitioned spatial smoothing(PSS) technique is used to construct a block covariance matrix, so as to decorrelate the coherency of signals. Then a signal subspace can be obtained by singular value decomposition(SVD) of the covariance matrix. Using the signal subspace, two extended signal subspaces are constructed to compensate aperture loss caused by PSS.The elevation angles can be estimated by estimation of signal parameter via rotational invariance techniques(ESPRIT) algorithm. At last, the estimated elevation angles can be used to estimate automatically paired azimuth angles. Compared with some other ESPRIT algorithms, the proposed algorithm shows higher estimation accuracy, which can be proved through the simulation results.

A feedback control method for phase signal demodulation in fber-optic hydrophones

In the realm of acoustic signal detection,the identification of weak signals,particularly in the presence of negative signal-to-noise ratios,poses a significant challenge.This challenge is further heightened when signals are acquired through fiber-optic hydrophones,as these signals often lack physical significance and resist clear systematic modeling.Conventional processing methods,e.g.,low-pass filter(LPF),require a thorough understanding of the effective signal bandwidth for noise reduction,and may introduce undesirable time lags.This paper introduces an innovative feedback control method with dual Kalman filters for the demodulation of phase signals with noises in fiber-optic hydrophones.A mathematical model of the closed-loop system is established to guide the design of the feedback control,aiming to achieve a balance with the input phase signal.The dual Kalman filters are instrumental in mitigating the effects of signal noise,observation noise,and control execution noise,thereby enabling precise estimation for the input phase signals.The effectiveness of this feedback control method is demonstrated through examples,showcasing the restoration of low-noise signals,negative signal-to-noise ratio signals,and multi-frequency signals.This research contributes to the technical advancement of high-performance devices,including fiber-optic hydrophones and phase-locked amplifiers.

Few-shot image recognition based on multi-scale features prototypical network

In order to improve the models capability in expressing features during few-shot learning,a multi-scale features prototypical network(MS-PN)algorithm is proposed.The metric learning algo-rithm is employed to extract image features and project them into a feature space,thus evaluating the similarity between samples based on their relative distances within the metric space.To sufficiently extract feature information from limited sample data and mitigate the impact of constrained data vol-ume,a multi-scale feature extraction network is presented to capture data features at various scales during the process of image feature extraction.Additionally,the position of the prototype is fine-tuned by assigning weights to data points to mitigate the influence of outliers on the experiment.The loss function integrates contrastive loss and label-smoothing to bring similar data points closer and separate dissimilar data points within the metric space.Experimental evaluations are conducted on small-sample datasets mini-ImageNet and CUB200-2011.The method in this paper can achieve higher classification accuracy.Specifically,in the 5-way 1-shot experiment,classification accuracy reaches 50.13%and 66.79%respectively on these two datasets.Moreover,in the 5-way 5-shot ex-periment,accuracy of 66.79%and 85.91%are observed,respectively.

Emotion Measurement Using Biometric Signal

In recent years, research on the estimation of human emotions has been active, and its application is expected in various fields. Biological reactions, such as electroencephalography (EEG) and root mean square successive difference (RMSSD), are indicators that are less influenced by individual arbitrariness. The present study used EEG and RMSSD signals to assess the emotions aroused by emotion-stimulating images in order to investigate whether various emotions are associated with characteristic biometric signal fluctuations. The participants underwent EEG and RMSSD while viewing emotionally stimulating images and answering the questionnaires. The emotions aroused by emotionally stimulating images were assessed by measuring the EEG signals and RMSSD values to determine whether different emotions are associated with characteristic biometric signal variations. Real-time emotion analysis software was used to identify the evoked emotions by describing them in the Circumplex Model of Affect based on the EEG signals and RMSSD values. Emotions other than happiness did not follow the Circumplex Model of Affect in this study. However, ventral attentional activity may have increased the RMSSD value for disgust as the β/θ value increased in right-sided brain waves. Therefore, the right-sided brain wave results are necessary when measuring disgust. Happiness can be assessed easily using the Circumplex Model of Affect for positive scene analysis. Improving the current analysis methods may facilitate the investigation of face-to-face communication in the future using biometric signals.

Special Section on High-Dimensional Signal Processing

Massive amounts of data are acquired in modern and future information technology industries such as communication,radar,and remote sensing.The presence of large dimensionality and size in these data offers new opportunities to enhance the performance of signal processing in such applications and even motivate new ones.However,the curse of dimensionality is always a challenge when processing such high-dimensional signals.In practical tasks,high-dimensional signals need to be acquired,processed,and analyzed with high accuracy,robustness,and computational efficiency.This special section aims to address these challenges,where articles attempt to develop new theories and methods that are best suited to the high dimensional nature of the signals involved,and explore modern and emerging applications in this area.